Anomaly detection in dynamic graphs attracts considerable interest by the research community [1]}. In recent years, many methods are presented on this topic.
For example, GOutlier [2]} employs a structural connectivity model to detect outliers in graph streams and builds dynamic network partition to maintain the connectivity behavior model.
CAD [3]} detects node relationships by tracking a measure that combines information regarding changes in graph structure and changes in edge weights.
CM-Sketch [4]} considers both the local structural information and historical behavior to discriminate the edge's anomalous property.
StreamSpot [5]} is a clustering-based approach that utilizes a novel similarity function for heterogeneous graphs property comparison and leverages a centroid-based clustering method to model the behaviors of graph stream.
SpotLight [6]} uses a randomized sketching technique to guarantee a large mapped distance between anomalous and normal instances in the sketch space.
Since these approaches leverage the shallow mechanisms to detect the anomalous edges, we categorize them into shallow learning-based methods.
